Advancing Renewable Electricity Consumption With Reinforcement Learning

• URL: http://arxiv.org/abs/2003.04310v1
• Date: Mon, 9 Mar 2020 20:57:58 GMT
• Title: Advancing Renewable Electricity Consumption With Reinforcement Learning
• Authors: Filip Tolovski
• Abstract summary: We propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation. We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation. We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions.

Related papers

• Green Multi-Objective Scheduling -- A memetic NSGA-III for flexible production with real-time energy cost and emissions [0.0]
  This study focuses on industries adjusting production to real-time energy markets, offering flexible consumption to the grid. We present a novel memetic NSGA-III to minimize makespan, energy cost, and emissions, integrating real energy market data.
  arXiv  Detail & Related papers  (2024-05-23T09:11:21Z)
• Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms [1.2289361708127877]
  This study aims to decrease dairy farms' dependence on traditional electricity grids by enabling the sale of surplus renewable energy in Peer-to-Peer markets. The Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES) has been developed, providing a platform to experiment with Reinforcement Learning techniques. The simulations demonstrate significant cost savings, including a 43% reduction in electricity expenses, a 42% decrease in peak demand, and a 1.91% increase in energy sales.
  arXiv  Detail & Related papers  (2024-05-21T12:19:17Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Sustainability using Renewable Electricity (SuRE) towards NetZero Emissions [0.0]
  Growth in energy demand poses serious threat to the environment. Most of the energy sources are non-renewable and based on fossil fuels, which leads to emission of harmful greenhouse gases. We present a scalable AI based solution that can be used by organizations to increase their overall renewable electricity share in total energy consumption.
  arXiv  Detail & Related papers  (2022-02-26T10:04:26Z)
• Uncertainty-Cognizant Model Predictive Control for Energy Management of Residential Buildings with PVT and Thermal Energy Storage [0.0]
  Building sector accounts for almost 40 percent of the global energy consumption. This paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected thermal photovoltaic (PVT) collectors.
  arXiv  Detail & Related papers  (2022-01-21T22:30:13Z)
• Modelling the transition to a low-carbon energy supply [91.3755431537592]
  A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world.
  arXiv  Detail & Related papers  (2021-09-25T12:37:05Z)
• Exploring market power using deep reinforcement learning for intelligent bidding strategies [69.3939291118954]
  We find that capacity has an impact on the average electricity price in a single year. The value of $sim$25% and $sim$11% may vary between market structures and countries. We observe that the use of a market cap of approximately double the average market price has the effect of significantly decreasing this effect and maintaining a competitive market.
  arXiv  Detail & Related papers  (2020-11-08T21:07:42Z)
• A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids [58.666456917115056]
  This paper presents a Reinforcement Learning based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers.
  arXiv  Detail & Related papers  (2020-09-23T02:17:51Z)
• A Survey on Smart Metering Systems using Blockchain for E-Mobility [0.0]
  With the arrival of electric vehicles, various challenges and opportunities are being presented in the electric power system worldwide. To achieve electric mobility, they must solve new challenges, such as the smart metering of energy consumption and the cybersecurity of these measurements.
  arXiv  Detail & Related papers  (2020-09-06T22:55:25Z)
• Towards a Peer-to-Peer Energy Market: an Overview [68.8204255655161]
  This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers. We introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid. To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability.
  arXiv  Detail & Related papers  (2020-03-02T20:32:10Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.